def predict(self, X):
        # This is just a place holder so that the code still runs.
        # Your code goes here.

        for row in range(0, len(X)):
            probTrue = long(1)
            probFalse = long(1)
            probE = 0
            for column in range(0, len(X[0])):
                if(X[row][column] == 1):
                    probTrue *= np.log((float(self.attributeCountTrue[column]) + 1) / (float(self.resultTrueCount) + 2))
                    probFalse *= np.log((float(self.attributeCountTrue[column]) + 1) / (float(self.resultFalseCount) + 2))
                if (X[row][column] == 0):
                    probTrue *= np.log(float(self.attributeCountFalse[column]) + 0) / (float(self.resultTrueCount) + 2)
                    probFalse *= np.log((float(self.attributeCountFalse[column]) + 0) / (float(self.resultFalseCount) + 2))
                #print "ProbTrue = ", probTrue

            probTrue *= (self.resultTrueCount/(np.log(self.resultTrueCount+self.resultFalseCount)))
            probFalse *= (self.resultFalseCount / (np.log(self.resultTrueCount + self.resultFalseCount)))

            probE = probTrue + probFalse
           #probTrue /= probE
            #probFalse /= probE
            print "ProbTrue = ", probTrue
            print "ProbFalse = ", probFalse
            #print "ProbTrue = ", probTrue
            #print "ProbFalse = ", probFalse
            print ""